Being a company funded partially by DARPA, it is very hard to find information about them. However, let's try to find out how they use technology to build their awesome robots. If someone from Boston Dynamics can correct my mistakes, I'd appreciate it. Eric Jang, Research Engineer at Google Brain, said "The value of Boston Dynamic is almost entirely in their closed-source control software. Boston Dynamics doesn't publish what techniques they use, but from Marc Raibert's talk at NIPS, it seems like their work is based on the approach proposed by "Sequential Composition of Dynamically Dexterous Robot Behaviors" by Burridge, Rizzi, and Koditscheck in 1999 (https://kodlab.seas.upenn.edu/up...). The robotic policy uses a model-based controller, which is in turn represented as a sequential composition of "cost funnels" that operate over local regions of state space."

In Part One of Vision publisher David Hulme's interview with Seán Ó hÉigeartaigh, the AI expert had much to say about the pros and cons of artificial intelligence. Part Two is a continuation of that discussion. Where is the developing technology leading us? DH At the end of the Second World War, Albert Einstein wrote a famous editorial in the New York Times. He's obviously regretting that the nuclear genie is out of the bottle. Scientists sometimes get to this point.

A Chinese probe is expected to touch down on the dark side of the moon on Thursday, according to the CGTN state broadcaster, in a groundbreaking mission in space exploration. China's space agency, however, has not confirmed the landing date for the unmanned Chang'e 4 lunar lander and rover spacecraft, which was launched on December 8, 2018 and is named after the Chinese goddess of the moon. If the manoeuvre is successful, China will become the first country to land an object on the far side of Earth's nearest celestial neighbour. The probe is carrying a robot vehicle that is to explore both above and below the lunar surface after arriving at the South Pole-Aitken basin's Von Karman crater. It is also equipped with a panorama camera and measuring devices to conduct experiments. It will perform radio-astronomical studies that, because the far side always faces away from Earth, will be "free from interference from our planet's ionosphere, human-made radio frequencies and auroral radiation noise", according to space industry expert Leonard David.

"On the face of it, this sounds like bad news for people," Branson admits (and Elon Musk certainly thinks so). But unemployment rates will be about the same in 20 years, even if the kind of jobs have changed, according to Rob Atkinson, founder and president of the Information Technology and Innovation Foundation writing in the Bureau of Labor Statistics' Monthly Labor Review. And if "governments and businesses are clever, the advance of technology could actually be really positive for people all over the world," says Branson. For example, governments should pay for workers to be retrained, Branson says, and there will need to be a way to keep people's income the same. But with solutions to such issues, more technology could help create "smarter working practices," says Branson. "Could people eventually take three and even four day weekends? I think so," he says.

It's the buzzword that's taken over most human resources (HR) conferences. At last year's HR Technology Conference and Expo in Las Vegas, several companies touted the integration of artificial intelligence (AI) into their recruiting products. Back home, the 2018 SHRM HR Tech Conference, held in Hyderabad in April, saw key sessions on how AI is going to be a driving force in HR functions. Their next conference in Chicago, held in June, had sessions on how to use AI to better understand employees. AI, or the ability of machines to imitate the human mind, is invading the workplace.

Theresa May's Brexit deal gives farmers certainty about the future, Environment Secretary Michael Gove will say. In a speech to the Oxford Farming Conference, Mr Gove will argue the agreement struck last year will ensure a smooth transition period for agriculture after the UK leaves the EU. He will also say Brexit will provide farmers with a "world of opportunity". Mr Gove will pledge investment in robotics, artificial intelligence and other innovation, to boost yields. In his speech, the environment secretary will promise to "continue to demonstrate the case for, and put in place the policies that underpin, long-term investment in British agriculture and the rural economy".

Silicon Valley should've been called Balloon Burg. America's tech industry often seems like it might pop under the slightest pressure. Now, according to The New York Times, many of these companies are looking at looming U.S. Department of Commerce export restrictions on artificial intelligence like an inflatable animal would look at a porcupine. Credit: Tartila / Shutterstock Here's the problem: Congress voted in August to limit the export of "emerging and foundational technologies" to preserve U.S. national security interests. The New York Times said that a Commerce Department proposal would restrict the export of "several categories of AI-like computer vision, speech recognition, and natural language understanding" to countries the U.S. has sanctioned in the past. Americans can voice their opinions about this proposal until January 10.

Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on a large scale. An additional complication is that the definition of an informal settlement is also very broad, which makes it a non-trivial task to collect data. This also makes it challenging to teach a machine what to look for. Due to these challenges we provide three contributions in this work. 1) A brand new machine learning data-set, purposely developed for informal settlement detection that contains a series of low and very-high resolution imagery, with accompanying ground truth annotations marking the locations of known informal settlements. 2) We demonstrate that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, which is typically cost-prohibitive for NGOs. 3) We demonstrate two effective classification schemes on our curated data set, one that is cost-efficient for NGOs and another that is cost-prohibitive for NGOs, but has additional utility. We integrate these schemes into a semi-automated pipeline that converts either a LR or VHR satellite image into a binary map that encodes the locations of informal settlements. We evaluate and compare our methods.

Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river discharge measurements, which can be quite scarce and unreliable, particularly in regions where floods cause the most damage every year. Accordingly, in this work we tackle the problem of river discharge estimation at different river locations. A core characteristic of the data at hand (e.g. satellite measurements) is that we have few measurements for many locations, all sharing the same physics that underlie the water discharge. We capture this scenario in a simple but powerful common mechanism regression (CMR) model with a local component as well as a shared one which captures the global discharge mechanism. The resulting learning objective is non-convex, but we show that we can find its global optimum by leveraging the power of joining local measurements across sites. In particular, using a spectral initialization with provable near-optimal accuracy, we can find the optimum using standard descent methods. We demonstrate the efficacy of our approach for the problem of discharge estimation using simulations.

Abstract--In this work we investigate approaches to reconstruct generator models from measurements available at the generator terminal bus using machine learning (ML) techniques. The goal is to develop an emulator which is trained online and is capable of fast predictive computations. The training is illustrated on synthetic data generated based on available open-source dynamical generator model. Two ML techniques were developed and tested: (a) standard vector auto-regressive (VAR) model; and (b) novel customized long short-term memory (LSTM) deep learning model. Tradeoffs in reconstruction ability between computationally light but linear AR model and powerful but computationally demanding LSTM model are established and analyzed.